In wireless mobile communication, often linear power amplifiers PA are required for radio frequency RF transmissions. However, in general, low power consumption is aspired for mobile systems. Therefore, a power amplifier may be operated at compressed regions.
In general, a power amplifier and an associated low power analog transmitter in radio devices behave non-linearly when operated at compressed regions. Since non-linearity may cause severe problems in regard of control of the system, it is expedient to eliminate or at least abate the non-linearity thereof. One possible approach that solves the non-linearity issue is to back off considerably so that the operation region becomes linear. However, this is very inefficient and does not yield the desired power savings.
Hence, a Digital Pre-Distortion (DPD) algorithm is often used in Radios that allows the RF signal to operate in the compression region. Operating in compressive regions will bring power savings due to increased efficiency. However, operating in such regions will also increase the inter modulation (IM) products. Increased IM products in general, violate the 3GPP specifications. Hence the primary role of the DPD (Digital Pre-Distortion) algorithm is to reduce the IM products so that the radio can operate efficiently in compliance with the 3GPP specifications.
A certain DPD algorithm belongs to a category called DLA (Direct Learning Algorithm). In DLA, the non-linearity is modeled from input to the output. An example would be the modeling of a power amplifier from input to the output. In other words, the output is described by input variables and the input signal.
Therefore DLA does not produce an inverse model of the power amplifier, rather it models the power amplifier directly. Hence, to obtain the inverse, an iterative process is normally pursued. This iterative inversion process is generally fixed to a pre-determined number of iterations (i.e. 2, 3, . . . 5 etc). Examples are the fixed point algorithms with N1 iterations or the Newton Method with N2 iterations. N1 and N2 are selected based on the required convergence accuracy of the inverse model. Another factor that limits N1 and N2 are hardware limitations.
However, it very common to have DLA implemented adaptively via a modified fixed point algorithm or a modified Newton method. The Newton method is extremely complex; hence a modified fixed point algorithm is more suited due to its simpler form compared with the Newton method.
In a wireless/cellular environment, a continuous adaptation of the DPD algorithm has to take place in anticipation of changes of the power amplifier. However, such a continuous adaptation can bring instability due to numerical error accumulation (e.g. floating point errors). This is because when adaptations tend to be very large, even a small numerical error per step can cause a huge accumulation of noise. Numerical error accumulation had been an ongoing problem that had prevented the best performance for the DLA algorithm.